def make_pipeline():
"""
A function to create our dynamic stock selector (pipeline). Documentation on
pipeline can be found here: https://www.quantopian.com/help#pipeline-title
"""
base_universe = Q1500US()
sector = Sector()
# screen is based off of returns
returns = Returns(window_length=2)
# check if stock price has good strength, but not necessarily overbought
rsi = RSI()
price = USEquityPricing.close.latest
# creating filter by specifying the type of returns desired
top_return_stocks = returns.top(1, mask=base_universe, groupby=sector)
pipe = Pipeline(
columns={
'rsi': rsi,
'price': price
},
# filter top return stocks, and stocks that are not being overbought
# but are not too oversold either
screen=base_universe & top_return_stocks & (20 < rsi < 80)
# the above is equivalent to: choose stocks from the base universe that have had the top returns in their sectors and have a good RSI value
)
return pipe
def make_pipeline(context):
# Use a universe of just the top 500 US stocks
universe = Q500US()
sector = Sector()
# Determine if the market is trending up or down. If the market is trending down then just buy bonds. If it is trending up then buy stocks
# This is not an optimal solution and will obviously not work in all future market crashes
spy_ma_fast_slice = SMA(
inputs=[USEquityPricing.close],
window_length=context.TF_FAST_LOOKBACK)[context.SPY]
spy_ma_slow_slice = SMA(
inputs=[USEquityPricing.close],
window_length=context.TF_SLOW_LOOKBACK)[context.SPY]
spy_ma_fast = SMA(inputs=[spy_ma_fast_slice], window_length=1)
spy_ma_slow = SMA(inputs=[spy_ma_slow_slice], window_length=1)
# If the 100 moving average crosses the 10 then alter the trend up variable
trend_up = spy_ma_fast > spy_ma_slow
# Get simple factors to determine "quality" companies
cash_return = ms.cash_return.latest.rank(mask=universe)
fcf_yield = ms.fcf_yield.latest.rank(mask=universe)
roic = ms.roic.latest.rank(mask=universe)
rev_growth = ms.revenue_growth.latest.rank(mask=universe)
value = (cash_return + fcf_yield).rank(mask=universe)
# Find sentiment rank using https://www.quantopian.com/posts/sentiment-as-a-factor
sentiment_score = SMA(
inputs=[sentiment.sentiment_signal],
window_length=30,
)
sentiment_score_zscore = sentiment_score.zscore()
sentiment_overall_rank = sentiment_score_zscore.rank(
mask=universe, groupby=sector).demean()
# Combine factors to create one single ranking system
quality = roic + rev_growth + value + sentiment_overall_rank
# Create a 'momentum' factor by looking back over the last 140 days (20 weeks).
momentum = Returns(window_length=context.MOMENTUM_LOOKBACK_DAYS)
# Filters for top quality and momentum to use in the selection criteria
top_quality = quality.top(context.TOP_ROE_QTY, mask=universe)
top_quality_momentum = momentum.top(context.TARGET_SECURITIES,
mask=top_quality)
# Only return values to be used in the selection criteria by using the screen parameter
pipe = Pipeline(columns={
'trend_up': trend_up,
'top_quality_momentum': top_quality_momentum,
},
screen=top_quality_momentum)
return pipe
def make_pipeline():
#0. Select top-1500 liquid stocks
base_universe = Q1500US()
#1. Market Capitalization Filter, select top 1200 out of 1500
market_cap = MarketCap(mask=base_universe)
top_market_cap = market_cap.top(1200)
#2. B/M Filter, select top 5000 out of all stocks (~8200)
btom = morningstar.valuation_ratios.book_value_yield.latest
top_btom = btom.top(5000)
#3. Interception of 1st and 2nd filters results, on average returns ~1000 stocks
top_cap_btom = top_market_cap & top_btom
#4. Top Performers filter, select top 100
latest_return_7_1 = Returns(
inputs=[USEquityPricing.close], window_length=147,
mask=top_cap_btom) / Returns(inputs=[USEquityPricing.close],
window_length=21,
mask=top_cap_btom)
top_performers = latest_return_7_1.top(100)
#5. Smoothest Returns filter, select 50 smoothest
r_7 = Returns(window_length=21, mask=top_performers)
r_6 = (Returns(window_length=42, mask=top_performers) + 1) / (r_7 + 1) - 1
r_5 = (Returns(window_length=63, mask=top_performers) + 1) / (
(r_7 + 1) * (r_6 + 1)) - 1
r_4 = (Returns(window_length=84, mask=top_performers) + 1) / (
(r_7 + 1) * (r_6 + 1) * (r_5 + 1)) - 1
r_3 = (Returns(window_length=105, mask=top_performers) + 1) / (
(r_7 + 1) * (r_6 + 1) * (r_5 + 1) * (r_4 + 1)) - 1
r_2 = (Returns(window_length=126, mask=top_performers) + 1) / (
(r_7 + 1) * (r_6 + 1) * (r_5 + 1) * (r_4 + 1) * (r_3 + 1)) - 1
r_1 = (Returns(window_length=147, mask=top_performers) + 1) / (
(r_7 + 1) * (r_6 + 1) * (r_5 + 1) * (r_4 + 1) * (r_3 + 1) *
(r_2 + 1)) - 1
r_mean = (r_1 + r_2 + r_3 + r_4 + r_5 + r_6) / 6.0
varr = ((r_1 - r_mean)**2 + (r_2 - r_mean)**2 + (r_3 - r_mean)**2 +
(r_4 - r_mean)**2 + (r_5 - r_mean)**2 + (r_6 - r_mean)**
2) / 6.0 #Sample variance of monthly returns of each stock
top_smooth = varr.bottom(50)
#6. Screening filter, if (tradeable) then buy this stock
tradeable = top_smooth
return Pipeline(screen=tradeable, columns={'Tradeable': tradeable})
def make_pipeline(context):
# Use a universe of just the top 500 US stocks
universe = Q500US()
# Get simple factors to determine "quality" companies
cash_return_latest = ms.cash_return.latest
fcf_yield_latest = ms.fcf_yield.latest
roic_latest = ms.roic.latest
rev_growth_latest = ms.revenue_growth.latest
cash_return = cash_return_latest.rank(mask=universe)
fcf_yield = fcf_yield_latest.rank(mask=universe)
roic = roic_latest.rank(mask=universe)
rev_growth = rev_growth_latest.rank(mask=universe)
value = (cash_return + fcf_yield).rank(mask=universe)
# Combine factors to create one single ranking system
quality = value + roic + rev_growth
# Create a 'momentum' factor by looking back over the last 140 days (20 weeks)
momentum = Returns(window_length=context.MOMENTUM_LOOKBACK_DAYS)
# Filters for top quality and momentum to use in the selection criteria
top_quality = quality.top(
context.TOP_ROE_QTY, mask=universe) & cash_return_latest.notnull(
) & fcf_yield_latest.notnull() & roic_latest.notnull(
) & rev_growth_latest.notnull()
top_quality_momentum = momentum.top(context.TARGET_SECURITIES,
mask=top_quality)
# Only return values to be used in the selection criteria by using the screen parameter
pipe = Pipeline(columns={
'top_quality_momentum': top_quality_momentum,
},
screen=top_quality_momentum)
return pipe