from quantopian.pipeline import Pipeline
from quantopian.pipeline.experimental import QTradableStocksUS

def make_pipeline():

    return Pipeline(
        columns={
            # Your pipeline columns go here.
        },
        screen=QTradableStocksUS()
    )

import quantopian.algorithm as algo
import quantopian.optimize as opt

MAX_SHORT_POSITION_SIZE = 0.01  # 1%
MAX_LONG_POSITION_SIZE = 0.01   # 1%

# Define the position concentration constraint.
constrain_pos_size = opt.PositionConcentration.with_equal_bounds(
    -MAX_SHORT_POSITION_SIZE,
    MAX_LONG_POSITION_SIZE,
)

# Supply the constraint to order_optimal_portfolio.
algo.order_optimal_portfolio(
    objective=my_objective, #Fill in with your objective function.
    constraints=[
        constrain_pos_size,
    ],
)

import quantopian.algorithm as algo
import quantopian.optimize as opt

# Define the dollar neutral constraint.
dollar_neutral = opt.DollarNeutral()

# Supply the constraint to order_optimal_portfolio.
algo.order_optimal_portfolio(
    objective=my_objective, #Fill in with your objective function.
    constraints=[
        dollar_neutral,
    ],
)

def initialize(context):
    # Schedule our reblance function to run every day, 45 minutes after
    # market open.
    schedule_function(
        func=my_rebalance,  
        date_rule=date_rules.every_day(),  
        time_rule=time_rules.market_open(hours=0,minutes=45)
    )

def my_rebalance(context, data):
    # Ordering/rebalance logic goes here.

import quantopian.algorithm as algo
import quantopian.optimize as opt

MAX_GROSS_LEVERAGE = 1.0  # 1x

# Define the max leverage constraint.
constrain_gross_leverage = opt.MaxGrossExposure(MAX_GROSS_LEVERAGE)

# Supply the constraint to order_optimal_portfolio.
algo.order_optimal_portfolio(
    objective=my_objective, #Fill in with your objective function.
    constraints=[
        constrain_gross_leverage,
    ],
)

import quantopian.algorithm as algo
import quantopian.optimize as opt

from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import SimpleBeta

def initialize(context):
    # Define the beta-to-SPY factor in Pipeline.
    beta = SimpleBeta(
                    target=sid(8554),
                    regression_length=260,
                    )

    pipe = Pipeline(
        columns={
            'beta': beta,
        },
        screen=beta.notnull(),
    )
    algo.attach_pipeline(pipe, 'pipe')

def before_trading_start(context, data):
    # Get the pipeline data every day.
    context.pipeline_data = algo.pipeline_output('pipe')

def do_portfolio_construction(context, data):
    pipeline_data = context.pipeline_data

    # Define the beta constraint to be +/- 0.05 beta-to-SPY based 
    # on historical per-asset beta.
    beta_neutral = opt.FactorExposure(
        pipeline_data[['beta']],
        min_exposures={'beta': -0.05},
        max_exposures={'beta': 0.05},
    )

    # Supply the constraint to order_optimal_portfolio.
    algo.order_optimal_portfolio(
        objective=my_objective, #Fill in with your objective function.
        constraints=[
            beta_neutral,
        ],
    )

import quantopian.algorithm as algo
import quantopian.optimize as opt

from quantopian.pipeline.experimental import risk_loading_pipeline  

def initialize(context):
    # Attach the risk loading pipeline to our algorithm.
    algo.attach_pipeline(risk_loading_pipeline(), 'risk_loading_pipeline')

def before_trading_start(context, data):
    # Get the risk loading data every day.
    context.risk_loading_pipeline = pipeline_output('risk_loading_pipeline')

def place_orders(context, data):  
    # Constrain our risk exposures. We're using version 0 of the default bounds
    # which constrain our portfolio to 18% exposure to each sector and 36% to
    # each style factor.
    constrain_sector_style_risk = opt.experimental.RiskModelExposure(  
        risk_model_loadings=context.risk_loading_pipeline,  
        version=0,
    )

    # Supply the constraint to order_optimal_portfolio.
    algo.order_optimal_portfolio(  
        objective=my_objective,  #Fill in with your objective function.
        constraints=[constrain_sector_style_risk],  
    )

constrain_sector_style_risk = opt.experimental.RiskModelExposure(  
    risk_model_loadings=context.risk_loading_pipeline,  
    version=0,  
    min_momentum=-0.1,  
    max_momentum=0.1,  
)