Quant-Strategies/algorithm_sentiment.py at master · vishal2106/Quant-Strategies · GitHub

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import quantopian.algorithm as algo
import quantopian.optimize as opt
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.psychsignal import stocktwits
from quantopian.pipeline.factors import SimpleMovingAverage
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.experimental import risk_loading_pipeline


def initialize(context):
    context.max_leverage = 1.0
    context.max_pos_size = 0.015
    context.max_turnover = 0.95


    algo.attach_pipeline(
        make_pipeline(),
        'data_pipe'
    )
    algo.attach_pipeline(
        risk_loading_pipeline(),
        'risk_pipe'
    )


    algo.schedule_function(
        rebalance,
        algo.date_rules.week_start(),
        algo.time_rules.market_open(),
    )


def before_trading_start(context, data):

    context.pipeline_data = algo.pipeline_output('data_pipe')

    context.risk_factor_betas = algo.pipeline_output('risk_pipe')


def make_pipeline():

    sentiment_score = SimpleMovingAverage(
        inputs=[stocktwits.bull_minus_bear],
        window_length=3,
        mask=QTradableStocksUS()
    )

    return Pipeline(
        columns={
            'sentiment_score': sentiment_score,
        },
        screen=sentiment_score.notnull()
    )


def rebalance(context, data):

    alpha = context.pipeline_data.sentiment_score

    if not alpha.empty:

        objective = opt.MaximizeAlpha(alpha)
        constrain_pos_size = opt.PositionConcentration.with_equal_bounds(
            -context.max_pos_size,
            context.max_pos_size
        )

        max_leverage = opt.MaxGrossExposure(context.max_leverage)
        dollar_neutral = opt.DollarNeutral()
        max_turnover = opt.MaxTurnover(context.max_turnover)

        factor_risk_constraints = opt.experimental.RiskModelExposure(
            context.risk_factor_betas,
            version=opt.Newest
        )

        algo.order_optimal_portfolio(
            objective=objective,
            constraints=[
                constrain_pos_size,
                max_leverage,
                dollar_neutral,
                max_turnover,
                factor_risk_constraints,
            ]
        )