LeveragedETF_Algo

LeveragedETF_Algo

import numpy as np
import math

ratios = []
for weight in np.arange(0.00,1.10, 0.10):
    ratios.append([weight, 1 - weight])

lookback = 84
sd_power = 2.5

def initialize(context):
    #avoid not executed orders
    set_slippage(slippage.FixedSlippage(spread=0.00001))  
    #set commissions    
    set_commission(commission.PerShare(cost=0.05, min_trade_cost=1.25))
    
    # ETFs 
    context.symbols = [sid(37514),   # SPXL
                       sid(38294)]   # TMF
  
    # Rebalance 
    schedule_function(
        reallocate,
        date_rules.month_end(),
        time_rules.market_close(minutes = 15)
    )
         
    
def reallocate(context, data):
    price_history = data.history(context.symbols,'price',lookback,'1d')
    returns = (price_history - price_history.shift(1)) / price_history.shift(1)  
    
    # Random initial values
    ratio = [0, 1]
    max_sharpe = -1000

    # Loop through each ratio
    for c in range(len(ratios)):
        temp_returns = returns.iloc[:, 0] * ratios[c][0] + returns.iloc[:, 1] * ratios[c][1]
                
        cum_return = np.prod(1 + temp_returns) - 1
        ann_return = cum_return ** (252 / lookback)
        ann_sd = temp_returns.std() * math.sqrt(252)        
           
        sharpe = ann_return / (ann_sd ** sd_power)        
            
        if sharpe > max_sharpe:
            ratio = ratios[c]
            max_sharpe = sharpe
   	
    order_target_percent(context.symbols[0], ratio[0])    
    order_target_percent(context.symbols[1], ratio[1])          
    
    record(context.symbols[0], ratio[0])
    record(context.symbols[1], ratio[1])