from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline import CustomFactor
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
import numpy as np
from collections import defaultdict
class momentum_factor_1(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 20
def compute(self, today, assets, out, close):
out[:] = close[-1]/close[0]
class momentum_factor_2(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 60
def compute(self, today, assets, out, close):
out[:] = close[-1]/close[0]
class momentum_factor_3(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 125
def compute(self, today, assets, out, close):
out[:] = close[-1]/close[0]
class momentum_factor_4(CustomFactor):
inputs = [USEquityPricing.close]
window_length = 252
def compute(self, today, assets, out, close):
out[:] = close[-1]/close[0]
class market_cap(CustomFactor):
inputs = [USEquityPricing.close, morningstar.valuation.shares_outstanding]
window_length = 1
def compute(self, today, assets, out, close, shares):
out[:] = close[-1] * shares[-1]
class efficiency_ratio(CustomFactor):
inputs = [USEquityPricing.close, USEquityPricing.high, USEquityPricing.low]
window_length = 252
def compute(self, today, assets, out, close, high, low):
lb = self.window_length
e_r = np.zeros(len(assets), dtype=np.float64)
a=np.array(([high[1:(lb):1]-low[1:(lb):1],abs(high[1:(lb):1]-close[0:(lb-1):1]),abs(low[1:(lb):1]-close[0:(lb-1):1])]))
b=a.T.max(axis=1)
c=b.sum(axis=1)
e_r=abs(close[-1]-close[0]) /c
out[:] = e_r
def initialize(context):
set_commission(commission.PerShare(cost=0.005, min_trade_cost=1.00))
schedule_function(func=rebalance, date_rule=date_rules.month_start(days_offset=5), time_rule=time_rules.market_open(), half_days=True)
schedule_function(close_orders,date_rule=date_rules.week_end(),time_rule=time_rules.market_close())
set_do_not_order_list(security_lists.leveraged_etf_list)
context.acc_leverage = 1.00
context.holdings =10
context.profit_taking_factor = 0.01
context.profit_target={}
context.profit_taken={}
context.entry_date={}
context.stop_pct = 0.75
context.stop_price = defaultdict(lambda:0)
pipe = Pipeline()
attach_pipeline(pipe, 'ranked_stocks')
factor1 = momentum_factor_1()
pipe.add(factor1, 'factor_1')
factor2 = momentum_factor_2()
pipe.add(factor2, 'factor_2')
factor3 = momentum_factor_3()
pipe.add(factor3, 'factor_3')
factor4 = momentum_factor_4()
pipe.add(factor4, 'factor_4')
factor5=efficiency_ratio()
pipe.add(factor5, 'factor_5')
mkt_screen = market_cap()
stocks = mkt_screen.top(3000)
factor_5_filter = factor5 > 0.031
total_filter = (stocks& factor_5_filter)
pipe.set_screen(total_filter)
factor1_rank = factor1.rank(mask=total_filter, ascending=False)
pipe.add(factor1_rank, 'f1_rank')
factor2_rank = factor2.rank(mask=total_filter, ascending=False)
pipe.add(factor2_rank, 'f2_rank')
factor3_rank = factor3.rank(mask=total_filter, ascending=False)
pipe.add(factor3_rank, 'f3_rank')
factor4_rank = factor4.rank(mask=total_filter, ascending=False)
pipe.add(factor4_rank, 'f4_rank')
combo_raw = (factor1_rank+factor2_rank+factor3_rank+factor4_rank)/4
pipe.add(combo_raw, 'combo_raw')
pipe.add(combo_raw.rank(mask=total_filter), 'combo_rank')
def before_trading_start(context, data):
context.output = pipeline_output('ranked_stocks')
ranked_stocks = context.output.fillna(0)
ranked_stocks = context.output[context.output.factor_1 > 0]
ranked_stocks = context.output[context.output.factor_2 > 0]
ranked_stocks = context.output[context.output.factor_3 > 0]
ranked_stocks = context.output[context.output.factor_4 > 0]
ranked_stocks = context.output[context.output.factor_5 > 0]
context.stock_list = ranked_stocks.sort(['combo_rank'], ascending=True).iloc[:context.holdings]
update_universe(context.stock_list.index)
def handle_data(context, data):
for stock in context.portfolio.positions:
price = data[stock].price
context.stop_price[stock] = max(context.stop_price[stock], context.stop_pct * price)
for stock in context.portfolio.positions:
if data[stock].price < context.stop_price[stock]:
order_target(stock, 0)
context.stop_price[stock] = 0
current_date = get_datetime()
record(leverage=context.account.leverage, positions=len(context.portfolio.positions))
for stock in context.portfolio.positions:
if (stock.end_date - current_date).days < 2:
order_target_percent(stock, 0.0)
print "Long List"
log.info("\n" + str(context.stock_list.sort(['combo_rank'], ascending=True).head(context.holdings)))
if data[stock].close_price > context.profit_target[stock]:
context.profit_target[stock] = data[stock].close_price*1.25
profit_taking_amount = context.portfolio.positions[stock].amount * context.profit_taking_factor
order_target(stock, profit_taking_amount)
def rebalance(context,data):
weight = context.acc_leverage / len(context.stock_list)
for stock in context.stock_list.index:
if stock in data:
if context.stock_list.factor_1[stock]>1:
if (stock.end_date - get_datetime()).days > 35:
if stock not in security_lists.leveraged_etf_list:
order_target_percent(stock, weight)
context.profit_target[stock] = data[stock].close_price*1.25
for stock in context.portfolio.positions.iterkeys():
if stock not in context.stock_list.index or context.stock_list.factor_1[stock]<=1:
order_target(stock, 0)
def close_orders(context, data):
orders = get_open_orders()
if orders:
for o in orders:
cancel_order(o)
