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Futures quantopian algo in quantconnect

Hi, I am trying to port this quantopian algo to quantconnect but not sure where to start. Quantopian does not do live trading for futures currently so I can only backtest this in quantopian  

####################################################################
# Futures momentum reversion trade algorithm
# By Naoki Nagai, May 2017
#####################################################################
import numpy as np
import scipy as sp
from quantopian.algorithm import order_optimal_portfolio
import quantopian.experimental.optimize as opt

def initialize(context):
# Futures to be traded by the algorithm, by asset class
context.futures_by_assetclass = {
'equities' : [
'SP', # S&P 500 Futures (US large cap)
'NK', # Nikkei 225 Futures (Japan)
],
'fixedincome': [
'TU', # 2yr Tbill
'TY', # TNote 10 yr
'US', # TBond 30 yr
'ED', # Eurodollar
],
'currencies' : [
'EC', # Euro
'JE', # Japanese YEN
],
'commodities' :[
'CL', # Light Sweet Crude Oil
'GC', # Gold
'NG', # Natural gas
'CN', # Corn
],
}

# This holds all continuous future objects as an array
context.futures = []
for assetclass in context.futures_by_assetclass:
for future in context.futures_by_assetclass[assetclass]:
context.futures.append(continuous_future(future))

# Window length for the trend. The algo checks the trend in this interval
context.window = 63 # 63 days = one quarter

# This arbitrary value determines the weight of futurues long and short
context.multiplier = 2250.

# Max leverage the algo can take
context.maxleverage = 2.0

# How certain you want to be the trend is there. Null hypothesis probability
context.pvalue = 0.15

# Rebalance every day, 30 minutes after market open
schedule_function(func=rebalance,
date_rule=date_rules.every_day(),
time_rule=time_rules.market_open(minutes=30))

# Record exposure by asset class everyday
schedule_function(record_exposure,
date_rules.every_day(),
time_rules.market_close())

def rebalance(context, data):
# Calculate slopes for each futures
prediction = calc_slopes(context, data)

# Get target weights to futures contracts based on slopes
target_weights = get_target_weights(context, data, prediction)

# Exposure is noted for logging and record() plotting
context.exposure = {}
text = ''
for contract in target_weights:
context.exposure[contract.root_symbol] = target_weights[contract]
if target_weights[contract] != 0:
text += "\n%+3.1f%% \t%s \t(%s)" % (target_weights[contract]*100, contract.symbol, contract.asset_name)
if text == '':
text = '\nNo positions to take'
log.info('Target position of today:' + text)

# Rebalance portfolio using optimaize API
order_optimal_portfolio(
opt.TargetPortfolioWeights(target_weights),
constraints=[opt.MaxGrossLeverage(context.maxleverage),],
universe=target_weights
)

def calc_slopes(context, data):
# Initialize output
prediction = {}

# Get pricing data of continuous futures
all_prices = data.history(context.futures, 'price', context.window + 1, '1d')

# Calculate daily returns for each continuous futures
all_returns = all_prices.pct_change()[1:]

# for each future, run regression to underestand the trend of price movement
for future in context.futures:

# Y-axis is the daily return
Y = np.array(all_returns[future])

# X-axis is -3, -2, -1, 0...
X = np.array(range(-len(Y)+1,1))

# Then, we get a and b where Y = a X + b
coef = sp.stats.linregress(X, Y)

# Initialize
prediction[future] = 0

# Return trend exists i.e. price momentum is accelerating with high probability
if (coef.pvalue < context.pvalue):

# Price momentumm is clear. Speed and acceleration is in same direction
if (coef.slope * coef.intercept > 0.):

# Then, predict the price trend should reverse
prediction[future] = -coef.slope * context.multiplier

return prediction

def get_target_weights(context, data, prediction):

# Target weights per contract
target_weights = {}

total = 0.
for future in context.futures:
total += prediction[future]

# Target weight for the most traded actual futures contract
for future in context.futures:

# Get the contract from the continuous futures object
contract = data.current(future, 'contract')

# If contract is tradable, assign weight
if contract and data.can_trade(contract):
target_weights[contract] = prediction[future] / max(total,1.0)

return target_weights

def record_exposure(context, data):
# Record net exposure to different asset classes for tracking
for assetclass in context.futures_by_assetclass:

# We add weights by asset class
asset_weight = 0.
for future in context.exposure:
if future in context.futures_by_assetclass[assetclass]:
asset_weight += context.exposure[future]

# Plot exposure in asset class
record(assetclass, asset_weight)

# Record gross leverage
record(leverage = context.account.leverage)

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Hi ak129301 

Please start with this thread:
Migrating from Quantopian to QuantConnect

It doesn't cover all the features in the current algorithm and you will need to learn Lean/QuantConnect API to complete the porting.

0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Update Backtest





0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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