from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
import datetime
import statsmodels.api as sm
import numpy as np
import pandas as pd

'''
Algo Logic: 
1) define market uptrend and downtrend by the 252 days slope.
2) Group all stocks by sectors
3) Filter all the stocks of each sector by a combination of these factors: growth, value, roic, quality and 1/volatility
4) if the market is uptrend buy cyclical and sensitive stocks
5) if the market is downtrend buy sensitive and defensive stocks
'''

def initialize(context):
    context.num_stocks = 3
    
    context.defensive_sectors = [205,206,207] #Consumer_defensive, HealthCare, Utilities
    context.cyclical_sectors = [101,102,103,104] # Basic Materials, Consumer Cyclical, Financials, Real Estate
    context.sensitive_sectors = [308,309,310,311] # Communication, Energy, Industrials, Technology
    context.sectors = context.defensive_sectors + context.cyclical_sectors + context.sensitive_sectors
    context.fundamental_factors = ['growth', 'value','quality','roic', 'volatility']
    context.weigth = 0.95/(11*context.num_stocks)
    set_commission(commission.PerShare(cost=0, min_trade_cost=0))
    set_slippage(slippage.FixedSlippage(spread=0))
    create_pipeline(context)
    schedule_function(func=rebalance, 
                      date_rule=date_rules.month_end(days_offset=0), 
                      time_rule=time_rules.market_open(hours=0,minutes=30), 
                      half_days=True)

    schedule_function(func = cancel_open_orders,
                     date_rule = date_rules.every_day(),
                     time_rule = time_rules.market_close(minutes=5),half_days=True )   
                  
def handle_data(context, data):
    # Record and plot the leverage, number of positions, and expsoure of our portfolio over time. 
    record(
           exposure=context.account.net_leverage, 
           longs = len([s for s in data if context.portfolio.positions[s].amount > 0]),
           shorts = len([s for s in data if context.portfolio.positions[s].amount < 0]),
           leverage=context.account.leverage)

    
def before_trading_start(context, data):
    
    context.output = pipeline_output('ranking_example')
    
    #~~~~~~ RESET DICTS AND LISTS ~~~~~~#
    
    context.long_list=[]
    weigths = {}

    #~~~~~~ FILTER OUT DELISTED STOCKS  ~~~~~~#
    context.output['end_date'] = [stock.end_date for stock in context.output.index]
    context.output = context.output[(context.output['end_date'] > (get_datetime()+datetime.timedelta(days=10)))]
    df = context.output
    

    
    #~~~~~~ SECOND FILTERING:  RANKING EACH SECTORS STOCKS ~~~~~~#
    grouped = df.groupby('sector')   
    for sector, group in grouped:
        
        if group['slope'].mean() > 0.0:  #trend up: buy it --> select good stocks 
            roic = group['roic'] > 0.0
            growth = group['growth'] > 0.0
            value = group['value'] > group['value'] .mean()
            quality = group['quality'] > group['quality'].mean()
            group = group[roic & growth & value & quality]

            group = group[context.fundamental_factors]                   
            normalized = group.apply(normalize)
            fund = normalized.sum(axis=1) 
            selected = fund.order(ascending=False).iloc[:context.num_stocks]
            long_stocks = selected.index.tolist()
            volatility = group['volatility'].loc[long_stocks]
            weigths[sector] = volatility
            
            if len(long_stocks) > 0:
                for stock in long_stocks:
                    context.long_list.append(stock)
            else:
                pass # No good stocks for this sector
                
        else:  
            pass # Sector has negative slope
    long_weigths = pd.DataFrame(weigths)
    long_weigths = long_weigths.sum(axis=1)
    context.weigths = long_weigths/long_weigths.sum()
    update_universe(context.long_list)
def rebalance(context,data):
    # Order our longs
    log.info("ordering longs")

    for long_stock in context.long_list:
        if long_stock in data:
            if long_stock in context.dont_buys:
                continue
            order_target_percent(long_stock, context.weigths.loc[long_stock])
      
    # Get rid of any stocks incorrectly held
    for security in context.portfolio.positions:
        if get_open_orders(security):
            continue
        if security in data:
            if security in context.dont_buys:
                continue
            if security not in context.long_list:
                order_target_percent(security, 0)

def create_pipeline(context):
    pipe_columns = {'roic': Value(), 'growth': Quality(), 'sector':Sector(), 'quality':Quality(), 'value':Value(),
                    'slope': Slope(window_length=252), 
                    'volatility': Volatility()}
    pipe = Pipeline(columns=pipe_columns)
    attach_pipeline(pipe, 'ranking_example')
    
    # We'll use this to screen out illiquid stocks...
    dollar_volume = AvgDailyDollarVolumeTraded()
    high_dollar_volume = dollar_volume > 10**7
    
    # ...and this to screen out penny stocks.
    sma_200 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=200)
    price = USEquityPricing.close.latest
    not_a_penny_stock = sma_200 > 5.0
    
    # Implement both of our screens.
    pipe.set_screen(high_dollar_volume & not_a_penny_stock)  
            
    # Used to avoid purchasing any leveraged ETFs 
    context.dont_buys = [sec for sec in security_lists.leveraged_etf_list]
    
def cancel_open_orders(context, data):
    for security in get_open_orders():
        for order in get_open_orders(security):
            cancel_order(order)
                      
class Value(CustomFactor):
    
    # Pre-declare inputs and window_length
    inputs = [morningstar.income_statement.ebit,
              morningstar.valuation.enterprise_value]
    window_length = 1
    
    def compute(self, today, assets, out, ebit, ev):
        out[:] = ebit[-1] / ev[-1]
        
        
#  Create a fundamental based custom "Quality" (profitability) factor
class Quality(CustomFactor):
    inputs = [morningstar.operation_ratios.roe]
    window_length = 1
    
    def compute(self, today, assets, out, roe):
        out[:] = roe[-1]
        
class Roic(CustomFactor):
    inputs = [morningstar.operation_ratios.roic]
    window_length = 1
    
    def compute(self, today, assets, out, roic):
        out[:] = roic[-1]
        
class Growth(CustomFactor):
    inputs = [morningstar.operation_ratios.revenue_growth]
    window_length = 1
    def compute(self, today, assets, out, growth):
        out[:] = growth[-1]      
        
class Sector(CustomFactor):
    inputs = [morningstar.asset_classification.morningstar_sector_code]
    window_length= 1
    def compute(self, today, assets, out, sector):
        out[:] = sector[-1]
        
class PEG(CustomFactor):
    inputs = [morningstar.valuation_ratios.peg_ratio]
    window_length = 1
    def compute(self, today, assets, out, peg):
        out[:] = peg[-1] 
        
class AvgDailyDollarVolumeTraded(CustomFactor):
    
    inputs = [USEquityPricing.close, USEquityPricing.volume]
    window_length = 20
    
    def compute(self, today, assets, out, close_price, volume):
        out[:] = np.mean(close_price * volume, axis=0)  
        
class Slope(CustomFactor):
    inputs = [USEquityPricing.close]
    def compute(self, today, assets, out, prices):
        Y = prices
        X = range(len(prices))
        A = sm.add_constant(X)
        results = sm.OLS(Y,A).fit()
        (b, a) =results.params
        slope = a / b * 252.0
        out[:] = slope
        
class PE_ratio(CustomFactor):
    inputs = [morningstar.valuation_ratios.pe_ratio]
    window_length= 1
    def compute(self, today, assets, out, pe):
        out[:] = pe[-1]

class Volatility(CustomFactor):    
    inputs = [USEquityPricing.close]
    window_length = 252
    
    def compute(self, today, assets, out, close):  
        close = pd.DataFrame(data=close, columns=assets) 
        # Since we are going to rank largest is best we need to invert the sdev.
        out[:] = 1 / np.log(close).diff().std()
        
normalize = lambda x : (x-x.mean())/x.std()                  

from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
import datetime
import statsmodels.api as sm
import numpy as np
import pandas as pd

'''
Algo Logic: 
1) define market uptrend and downtrend by the 252 days slope.
2) Group all stocks by sectors
3) Filter all the stocks of each sector by a combination of these factors: growth, value, roic, quality and 1/volatility
4) if the market is uptrend buy cyclical and sensitive stocks
5) if the market is downtrend buy sensitive and defensive stocks
'''

def initialize(context):
    context.num_stocks = 3
    
    context.defensive_sectors = [205,206,207] #Consumer_defensive, HealthCare, Utilities
    context.cyclical_sectors = [101,102,103,104] # Basic Materials, Consumer Cyclical, Financials, Real Estate
    context.sensitive_sectors = [308,309,310,311] # Communication, Energy, Industrials, Technology
    context.sectors = context.defensive_sectors + context.cyclical_sectors + context.sensitive_sectors
    context.fundamental_factors = ['growth', 'value','quality','roic', 'volatility']
    context.weigth = 0.95/(11*context.num_stocks)
    set_commission(commission.PerShare(cost=0, min_trade_cost=0))
    set_slippage(slippage.FixedSlippage(spread=0))
    create_pipeline(context)
    schedule_function(func=rebalance, 
                      date_rule=date_rules.month_end(days_offset=0), 
                      time_rule=time_rules.market_open(hours=0,minutes=30), 
                      half_days=True)

    schedule_function(func = cancel_open_orders,
                     date_rule = date_rules.every_day(),
                     time_rule = time_rules.market_close(minutes=5),half_days=True )   
                  
def handle_data(context, data):
    # Record and plot the leverage, number of positions, and expsoure of our portfolio over time. 
    record(
           exposure=context.account.net_leverage, 
           longs = len([s for s in data if context.portfolio.positions[s].amount > 0]),
           shorts = len([s for s in data if context.portfolio.positions[s].amount < 0]),
           leverage=context.account.leverage,
    length_long_list= len(context.long_list) )

    
def before_trading_start(context, data):
    
    context.output = pipeline_output('ranking_example')
    
    #~~~~~~ RESET DICTS AND LISTS ~~~~~~#
    
    context.long_list=[]
    weigths = {}

    #~~~~~~ FILTER OUT DELISTED STOCKS  ~~~~~~#
    context.output['end_date'] = [stock.end_date for stock in context.output.index]
    context.output = context.output[(context.output['end_date'] > (get_datetime()+datetime.timedelta(days=10)))]
    df = context.output
    

    
    #~~~~~~ SECOND FILTERING:  RANKING EACH SECTORS STOCKS ~~~~~~#
    grouped = df.groupby('sector')   
    for sector, group in grouped:
        
        if group['slope'].mean() > 0.0:  #trend up: buy it --> select good stocks 
            roic = group['roic'] > 0.0
            growth = group['growth'] > 0.0
            value = group['value'] > group['value'] .mean()
            quality = group['quality'] > group['quality'].mean()
            group = group[roic & growth & value & quality]

            group = group[context.fundamental_factors]                   
            normalized = group.apply(normalize)
            fund = normalized.sum(axis=1) 
            selected = fund.order(ascending=False).iloc[:context.num_stocks]
            long_stocks = selected.index.tolist()
            volatility = group['volatility'].loc[long_stocks]
            weigths[sector] = volatility
            
            if len(long_stocks) > 0:
                for stock in long_stocks:
                    context.long_list.append(stock)
            else:
                pass # No good stocks for this sector
                
        else:  
            pass # Sector has negative slope
    long_weigths = pd.DataFrame(weigths)
    long_weigths = long_weigths.sum(axis=1)
    context.weigths = long_weigths/long_weigths.sum()
    update_universe(context.long_list)
def rebalance(context,data):
    print 'length of long list: {}'.format(len(context.long_list))
    # Order our longs
    log.info("ordering longs")

    for long_stock in context.long_list:
        if long_stock in data:
            if long_stock in context.dont_buys:
                continue
            order_target_percent(long_stock, context.weigths.loc[long_stock])
      
    # Get rid of any stocks incorrectly held
    for security in context.portfolio.positions:
        if get_open_orders(security):
            continue
        if security in data:
            if security in context.dont_buys:
                continue
            if security not in context.long_list:
                order_target_percent(security, 0)

def create_pipeline(context):
    pipe_columns = {'roic': Value(), 'growth': Quality(), 'sector':Sector(), 'quality':Quality(), 'value':Value(),
                    'slope': Slope(window_length=252), 
                    'volatility': Volatility()}
    pipe = Pipeline(columns=pipe_columns)
    attach_pipeline(pipe, 'ranking_example')
    
    # We'll use this to screen out illiquid stocks...
    dollar_volume = AvgDailyDollarVolumeTraded()
    high_dollar_volume = dollar_volume > 10**7
    
    # ...and this to screen out penny stocks.
    sma_200 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=200)
    price = USEquityPricing.close.latest
    not_a_penny_stock = sma_200 > 5.0
    
    # Implement both of our screens.
    pipe.set_screen(high_dollar_volume & not_a_penny_stock)  
            
    # Used to avoid purchasing any leveraged ETFs 
    context.dont_buys = [sec for sec in security_lists.leveraged_etf_list]
    
def cancel_open_orders(context, data):
    for security in get_open_orders():
        for order in get_open_orders(security):
            cancel_order(order)
                      
class Value(CustomFactor):
    
    # Pre-declare inputs and window_length
    inputs = [morningstar.income_statement.ebit,
              morningstar.valuation.enterprise_value]
    window_length = 1
    
    def compute(self, today, assets, out, ebit, ev):
        out[:] = ebit[-1] / ev[-1]
        
        
#  Create a fundamental based custom "Quality" (profitability) factor
class Quality(CustomFactor):
    inputs = [morningstar.operation_ratios.roe]
    window_length = 1
    
    def compute(self, today, assets, out, roe):
        out[:] = roe[-1]
        
class Roic(CustomFactor):
    inputs = [morningstar.operation_ratios.roic]
    window_length = 1
    
    def compute(self, today, assets, out, roic):
        out[:] = roic[-1]
        
class Growth(CustomFactor):
    inputs = [morningstar.operation_ratios.revenue_growth]
    window_length = 1
    def compute(self, today, assets, out, growth):
        out[:] = growth[-1]      
        
class Sector(CustomFactor):
    inputs = [morningstar.asset_classification.morningstar_sector_code]
    window_length= 1
    def compute(self, today, assets, out, sector):
        out[:] = sector[-1]
        
class PEG(CustomFactor):
    inputs = [morningstar.valuation_ratios.peg_ratio]
    window_length = 1
    def compute(self, today, assets, out, peg):
        out[:] = peg[-1] 
        
class AvgDailyDollarVolumeTraded(CustomFactor):
    
    inputs = [USEquityPricing.close, USEquityPricing.volume]
    window_length = 20
    
    def compute(self, today, assets, out, close_price, volume):
        out[:] = np.mean(close_price * volume, axis=0)  
        
class Slope(CustomFactor):
    inputs = [USEquityPricing.close]
    def compute(self, today, assets, out, prices):
        Y = prices
        X = range(len(prices))
        A = sm.add_constant(X)
        results = sm.OLS(Y,A).fit()
        (b, a) =results.params
        slope = a / b * 252.0
        out[:] = slope
        
class PE_ratio(CustomFactor):
    inputs = [morningstar.valuation_ratios.pe_ratio]
    window_length= 1
    def compute(self, today, assets, out, pe):
        out[:] = pe[-1]

class Volatility(CustomFactor):    
    inputs = [USEquityPricing.close]
    window_length = 252
    
    def compute(self, today, assets, out, close):  
        close = pd.DataFrame(data=close, columns=assets) 
        # Since we are going to rank largest is best we need to invert the sdev.
        out[:] = 1 / np.log(close).diff().std()
        
normalize = lambda x : (x-x.mean())/x.std()                  

from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
import datetime
import statsmodels.api as sm
import numpy as np
import pandas as pd

'''
Algo Logic: 
1) define market uptrend and downtrend by the 252 days slope.
2) Group all stocks by sectors
3) Filter all the stocks of each sector by a combination of these factors: growth, value, roic, quality and 1/volatility
4) if the market is uptrend buy cyclical and sensitive stocks
5) if the market is downtrend buy sensitive and defensive stocks
'''

def initialize(context):
    context.num_stocks = 3
    
    context.defensive_sectors = [205,206,207] #Consumer_defensive, HealthCare, Utilities
    context.cyclical_sectors = [101,102,103,104] # Basic Materials, Consumer Cyclical, Financials, Real Estate
    context.sensitive_sectors = [308,309,310,311] # Communication, Energy, Industrials, Technology
    context.sectors = context.defensive_sectors + context.cyclical_sectors + context.sensitive_sectors
    context.fundamental_factors = ['growth', 'value','quality','roic', 'volatility']
    context.weigth = 0.95/(11*context.num_stocks)
    set_commission(commission.PerShare(cost=0, min_trade_cost=0))
    set_slippage(slippage.FixedSlippage(spread=0))
    create_pipeline(context)
    schedule_function(func=rebalance, 
                      date_rule=date_rules.month_end(days_offset=0), 
                      time_rule=time_rules.market_open(hours=0,minutes=30), 
                      half_days=True)

    schedule_function(func = cancel_open_orders,
                     date_rule = date_rules.every_day(),
                     time_rule = time_rules.market_close(minutes=5),half_days=True )   
                  
def handle_data(context, data):
    # Record and plot the leverage, number of positions, and expsoure of our portfolio over time. 
    record(
           exposure=context.account.net_leverage, 
           longs = len([s for s in data if context.portfolio.positions[s].amount > 0]),
           shorts = len([s for s in data if context.portfolio.positions[s].amount < 0]),
           leverage=context.account.leverage,
    length_long_list= len(context.long_list) )

    
def before_trading_start(context, data):
    
    context.output = pipeline_output('ranking_example')
    
    #~~~~~~ RESET DICTS AND LISTS ~~~~~~#
    
    context.long_list=[]
    weigths = {}

    #~~~~~~ FILTER OUT DELISTED STOCKS  ~~~~~~#
    context.output['end_date'] = [stock.end_date for stock in context.output.index]
    context.output = context.output[(context.output['end_date'] > (get_datetime()+datetime.timedelta(days=10)))]
    df = context.output
    

    
    #~~~~~~ SECOND FILTERING:  RANKING EACH SECTORS STOCKS ~~~~~~#
    grouped = df.groupby('sector')   
    for sector, group in grouped:
        
        if group['slope'].mean() > 0.0:  #trend up: buy it --> select good stocks 
            roic = group['roic'] > 0.0
            growth = group['growth'] > 0.0
            value = group['value'] > group['value'] .mean()
            quality = group['quality'] > group['quality'].mean()
            group = group[roic & growth & value & quality]

            group = group[context.fundamental_factors]                   
            normalized = group.apply(normalize)
            fund = normalized.sum(axis=1) 
            selected = fund.order(ascending=False).iloc[:context.num_stocks]
            long_stocks = selected.index.tolist()
            volatility = group['volatility'].loc[long_stocks]
            weigths[sector] = volatility
            
            if len(long_stocks) > 0:
                for stock in long_stocks:
                    context.long_list.append(stock)
            else:
                pass # No good stocks for this sector
                
        else:  
            pass # Sector has negative slope
    long_weigths = pd.DataFrame(weigths)
    long_weigths = long_weigths.sum(axis=1)
    context.weigths = long_weigths/long_weigths.sum()
    update_universe(context.long_list)
def rebalance(context,data):
    print 'length of long list: {}'.format(len(context.long_list))
    # Order our longs
    log.info("ordering longs")

    for long_stock in context.long_list:
        if long_stock in data:
            if long_stock in context.dont_buys:
                continue
            order_target_percent(long_stock, context.weigths.loc[long_stock])
      
    # Get rid of any stocks incorrectly held
    for security in context.portfolio.positions:
        if get_open_orders(security):
            continue
        if security in data:
            if security in context.dont_buys:
                continue
            if security not in context.long_list:
                order_target_percent(security, 0)

def create_pipeline(context):
    pipe_columns = {'roic': Value(), 'growth': Quality(), 'sector':Sector(), 'quality':Quality(), 'value':Value(),
                    'slope': Slope(window_length=252), 
                    'volatility': Volatility()}
    pipe = Pipeline(columns=pipe_columns)
    attach_pipeline(pipe, 'ranking_example')
    
    # We'll use this to screen out illiquid stocks...
    dollar_volume = AvgDailyDollarVolumeTraded()
    high_dollar_volume = dollar_volume > 10**7
    
    # ...and this to screen out penny stocks.
    sma_200 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=200)
    price = USEquityPricing.close.latest
    not_a_penny_stock = sma_200 > 5.0
    
    # Implement both of our screens.
    pipe.set_screen(high_dollar_volume & not_a_penny_stock)  
            
    # Used to avoid purchasing any leveraged ETFs 
    context.dont_buys = [sec for sec in security_lists.leveraged_etf_list]
    
def cancel_open_orders(context, data):
    for security in get_open_orders():
        for order in get_open_orders(security):
            cancel_order(order)
                      
class Value(CustomFactor):
    
    # Pre-declare inputs and window_length
    inputs = [morningstar.income_statement.ebit,
              morningstar.valuation.enterprise_value]
    window_length = 1
    
    def compute(self, today, assets, out, ebit, ev):
        out[:] = ebit[-1] / ev[-1]
        
        
#  Create a fundamental based custom "Quality" (profitability) factor
class Quality(CustomFactor):
    inputs = [morningstar.operation_ratios.roe]
    window_length = 1
    
    def compute(self, today, assets, out, roe):
        out[:] = roe[-1]
        
class Roic(CustomFactor):
    inputs = [morningstar.operation_ratios.roic]
    window_length = 1
    
    def compute(self, today, assets, out, roic):
        out[:] = roic[-1]
        
class Growth(CustomFactor):
    inputs = [morningstar.operation_ratios.revenue_growth]
    window_length = 1
    def compute(self, today, assets, out, growth):
        out[:] = growth[-1]      
        
class Sector(CustomFactor):
    inputs = [morningstar.asset_classification.morningstar_sector_code]
    window_length= 1
    def compute(self, today, assets, out, sector):
        out[:] = sector[-1]
        
class PEG(CustomFactor):
    inputs = [morningstar.valuation_ratios.peg_ratio]
    window_length = 1
    def compute(self, today, assets, out, peg):
        out[:] = peg[-1] 
        
class AvgDailyDollarVolumeTraded(CustomFactor):
    
    inputs = [USEquityPricing.close, USEquityPricing.volume]
    window_length = 20
    
    def compute(self, today, assets, out, close_price, volume):
        out[:] = np.mean(close_price * volume, axis=0)  
        
class Slope(CustomFactor):
    inputs = [USEquityPricing.close]
    def compute(self, today, assets, out, prices):
        Y = prices
        X = range(len(prices))
        A = sm.add_constant(X)
        results = sm.OLS(Y,A).fit()
        (b, a) =results.params
        slope = a / b * 252.0
        out[:] = slope
        
class PE_ratio(CustomFactor):
    inputs = [morningstar.valuation_ratios.pe_ratio]
    window_length= 1
    def compute(self, today, assets, out, pe):
        out[:] = pe[-1]

class Volatility(CustomFactor):    
    inputs = [USEquityPricing.close]
    window_length = 252
    
    def compute(self, today, assets, out, close):  
        close = pd.DataFrame(data=close, columns=assets) 
        # Since we are going to rank largest is best we need to invert the sdev.
        out[:] = 1 / np.log(close).diff().std()
        
normalize = lambda x : (x-x.mean())/x.std()                  

from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import CustomFactor, SimpleMovingAverage
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
import datetime
import statsmodels.api as sm
import numpy as np
import pandas as pd

'''
Algo Logic: 
1) define market uptrend and downtrend by the 252 days slope.
2) Group all stocks by sectors
3) Filter all the stocks of each sector by a combination of these factors: growth, value, roic, quality and 1/volatility
4) if the market is uptrend buy cyclical and sensitive stocks
5) if the market is downtrend buy sensitive and defensive stocks
'''

def initialize(context):
    context.num_stocks = 3
    
    context.defensive_sectors = [205,206,207] #Consumer_defensive, HealthCare, Utilities
    context.cyclical_sectors = [101,102,103,104] # Basic Materials, Consumer Cyclical, Financials, Real Estate
    context.sensitive_sectors = [308,309,310,311] # Communication, Energy, Industrials, Technology
    context.sectors = context.defensive_sectors + context.cyclical_sectors + context.sensitive_sectors
    context.fundamental_factors = ['growth', 'value','quality','roic', 'volatility']
    context.weigth = 0.95/(11*context.num_stocks)
    set_commission(commission.PerShare(cost=0, min_trade_cost=0))
    set_slippage(slippage.FixedSlippage(spread=0))
    create_pipeline(context)
    schedule_function(func=rebalance, 
                      date_rule=date_rules.month_end(days_offset=0), 
                      time_rule=time_rules.market_open(hours=0,minutes=30), 
                      half_days=True)

    schedule_function(func = cancel_open_orders,
                     date_rule = date_rules.every_day(),
                     time_rule = time_rules.market_close(minutes=5),half_days=True )   
                  
def handle_data(context, data):
    if not context.run and len(data) <= 1:
        return
    # Record and plot the leverage, number of positions, and expsoure of our portfolio over time. 
    record(
           exposure=context.account.net_leverage, 
           longs = len([s for s in data if context.portfolio.positions[s].amount > 0]),
           shorts = len([s for s in data if context.portfolio.positions[s].amount < 0]),
           leverage=context.account.leverage,
    length_long_list= len(context.long_list) )

    
def before_trading_start(context, data):
    
    context.output = pipeline_output('ranking_example')
    
    #~~~~~~ RESET DICTS AND LISTS ~~~~~~#
    
    context.long_list=[]
    weigths = {}

    #~~~~~~ FILTER OUT DELISTED STOCKS  ~~~~~~#
    context.output['end_date'] = [stock.end_date for stock in context.output.index]
    context.output = context.output[(context.output['end_date'] > (get_datetime()+datetime.timedelta(days=10)))]
    df = context.output
    

    
    #~~~~~~ SECOND FILTERING:  RANKING EACH SECTORS STOCKS ~~~~~~#
    grouped = df.groupby('sector')   
    for sector, group in grouped:
        
        if group['slope'].mean() > 0.0:  #trend up: buy it --> select good stocks 
            roic = group['roic'] > 0.0
            growth = group['growth'] > 0.0
            value = group['value'] > group['value'] .mean()
            quality = group['quality'] > group['quality'].mean()
            group = group[roic & growth & value & quality]

            group = group[context.fundamental_factors]                   
            normalized = group.apply(normalize)
            fund = normalized.sum(axis=1) 
            selected = fund.order(ascending=False).iloc[:context.num_stocks]
            long_stocks = selected.index.tolist()
            volatility = group['volatility'].loc[long_stocks]
            weigths[sector] = volatility
            
            if len(long_stocks) > 0:
                for stock in long_stocks:
                    context.long_list.append(stock)
            else:
                pass # No good stocks for this sector
                
        else:  
            pass # Sector has negative slope
    long_weigths = pd.DataFrame(weigths)
    long_weigths = long_weigths.sum(axis=1)
    context.weigths = long_weigths/long_weigths.sum()
    if len(context.long_list) > 0:
        update_universe(context.long_list)
        context.run = True
    else:
        update_universe([sid(8554)])
        context.run = False
    
def rebalance(context,data):
    if not context.run and len(data) <= 1:
        return
    print 'here'
    print 'length of long list: {}'.format(len(context.long_list))
    # Order our longs
    log.info("ordering longs")

    for long_stock in context.long_list:
        if long_stock in data:
            if long_stock in context.dont_buys:
                continue
            order_target_percent(long_stock, context.weigths.loc[long_stock])
      
    # Get rid of any stocks incorrectly held
    for security in context.portfolio.positions:
        if get_open_orders(security):
            continue
        if security in data:
            if security in context.dont_buys:
                continue
            if security not in context.long_list:
                order_target_percent(security, 0)

def create_pipeline(context):
    pipe_columns = {'roic': Value(), 'growth': Quality(), 'sector':Sector(), 'quality':Quality(), 'value':Value(),
                    'slope': Slope(window_length=252), 
                    'volatility': Volatility()}
    pipe = Pipeline(columns=pipe_columns)
    attach_pipeline(pipe, 'ranking_example')
    
    # We'll use this to screen out illiquid stocks...
    dollar_volume = AvgDailyDollarVolumeTraded()
    high_dollar_volume = dollar_volume > 10**7
    
    # ...and this to screen out penny stocks.
    sma_200 = SimpleMovingAverage(inputs=[USEquityPricing.close], window_length=200)
    price = USEquityPricing.close.latest
    not_a_penny_stock = sma_200 > 5.0
    
    # Implement both of our screens.
    pipe.set_screen(high_dollar_volume & not_a_penny_stock)  
            
    # Used to avoid purchasing any leveraged ETFs 
    context.dont_buys = [sec for sec in security_lists.leveraged_etf_list]
    
def cancel_open_orders(context, data):
    if not context.run and len(data) <= 1:
        return
    for security in get_open_orders():
        for order in get_open_orders(security):
            cancel_order(order)
                      
class Value(CustomFactor):
    
    # Pre-declare inputs and window_length
    inputs = [morningstar.income_statement.ebit,
              morningstar.valuation.enterprise_value]
    window_length = 1
    
    def compute(self, today, assets, out, ebit, ev):
        out[:] = ebit[-1] / ev[-1]
        
        
#  Create a fundamental based custom "Quality" (profitability) factor
class Quality(CustomFactor):
    inputs = [morningstar.operation_ratios.roe]
    window_length = 1
    
    def compute(self, today, assets, out, roe):
        out[:] = roe[-1]
        
class Roic(CustomFactor):
    inputs = [morningstar.operation_ratios.roic]
    window_length = 1
    
    def compute(self, today, assets, out, roic):
        out[:] = roic[-1]
        
class Growth(CustomFactor):
    inputs = [morningstar.operation_ratios.revenue_growth]
    window_length = 1
    def compute(self, today, assets, out, growth):
        out[:] = growth[-1]      
        
class Sector(CustomFactor):
    inputs = [morningstar.asset_classification.morningstar_sector_code]
    window_length= 1
    def compute(self, today, assets, out, sector):
        out[:] = sector[-1]
        
class PEG(CustomFactor):
    inputs = [morningstar.valuation_ratios.peg_ratio]
    window_length = 1
    def compute(self, today, assets, out, peg):
        out[:] = peg[-1] 
        
class AvgDailyDollarVolumeTraded(CustomFactor):
    
    inputs = [USEquityPricing.close, USEquityPricing.volume]
    window_length = 20
    
    def compute(self, today, assets, out, close_price, volume):
        out[:] = np.mean(close_price * volume, axis=0)  
        
class Slope(CustomFactor):
    inputs = [USEquityPricing.close]
    def compute(self, today, assets, out, prices):
        Y = prices
        X = range(len(prices))
        A = sm.add_constant(X)
        results = sm.OLS(Y,A).fit()
        (b, a) =results.params
        slope = a / b * 252.0
        out[:] = slope
        
class PE_ratio(CustomFactor):
    inputs = [morningstar.valuation_ratios.pe_ratio]
    window_length= 1
    def compute(self, today, assets, out, pe):
        out[:] = pe[-1]

class Volatility(CustomFactor):    
    inputs = [USEquityPricing.close]
    window_length = 252
    
    def compute(self, today, assets, out, close):  
        close = pd.DataFrame(data=close, columns=assets) 
        # Since we are going to rank largest is best we need to invert the sdev.
        out[:] = 1 / np.log(close).diff().std()
        
normalize = lambda x : (x-x.mean())/x.std()