I'm running a full backtest on the algorithm given in Lecture 46. I get a Tradable Universe error:
To be eligible for the contest, algorithms must trade within the
Quantopian Tradable Universe.
So I change my code to this
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
"""
# Factor of yesterday's close price.
yesterday_close = USEquityPricing.close.latest
pipe = Pipeline(
columns={
'close': yesterday_close,
},
screen=QTradableStocksUS()
)
return pipe
However, I still get the same error. How do I fix this?
Below is the complete algorithm:
"""
This is a template algorithm on Quantopian for you to adapt and fill in.
"""
import quantopian.algorithm as algo
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS
import pandas as pd
import numpy as np
import statsmodels.api as sm
import quantopian.optimize as opt
from quantopian.algorithm import attach_pipeline
def initialize(context):
"""
Called once at the start of the algorithm.
"""
# Quantopian backtester specific variables
set_slippage(slippage.FixedSlippage(spread=0))
set_commission(commission.PerTrade(cost=1))
set_symbol_lookup_date('2014-01-01')
context.stock_pairs = [(symbol('ABGB'), symbol('FSLR')),
(symbol('CSUN'), symbol('ASTI'))]
context.stocks = symbols('ABGB', 'FSLR', 'CSUN', 'ASTI')
context.num_pairs = len(context.stock_pairs)
# strategy specific variables
context.lookback = 20 # used for regression
context.z_window = 20 # used for zscore calculation, must be <= lookback
context.target_weights = pd.Series(index=context.stocks, data=0.25)
context.spread = np.ndarray((context.num_pairs, 0))
context.inLong = [False] * context.num_pairs
context.inShort = [False] * context.num_pairs
# Only do work 30 minutes before close
schedule_function(func=check_pair_status, date_rule=date_rules.every_day(),
time_rule=time_rules.market_close(minutes=30))
# Create our pipeline and attach it to our algorithm.
my_pipe = make_pipeline()
attach_pipeline(my_pipe, 'pipeline')
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
"""
# Factor of yesterday's close price.
yesterday_close = USEquityPricing.close.latest
pipe = Pipeline(
columns={
'close': yesterday_close,
},
screen=QTradableStocksUS()
)
return pipe
def before_trading_start(context, data):
"""
Called every day before market open.
"""
context.output = algo.pipeline_output('pipeline')
# These are the securities that we are interested in trading each day.
context.security_list = context.output.index
def rebalance(context, data):
"""
Execute orders according to our schedule_function() timing.
"""
pass
def record_vars(context, data):
"""
Plot variables at the end of each day.
"""
pass
# Will be called on every trade event for the securities you specify.
def handle_data(context, data):
"""
Called every minute.
"""
# Our work is now scheduled in check_pair_status
pass
def check_pair_status(context, data):
prices = data.history(context.stocks, 'price', 35, '1d').iloc[-context.lookback::]
new_spreads = np.ndarray((context.num_pairs, 1))
for i in range(context.num_pairs):
(stock_y, stock_x) = context.stock_pairs[i]
Y = prices[stock_y]
X = prices[stock_x]
# Comment explaining try block
try:
hedge = hedge_ratio(Y, X, add_const=True)
except ValueError as e:
log.debug(e)
return
context.target_weights = get_current_portfolio_weights(context, data)
new_spreads[i, :] = Y[-1] - hedge * X[-1]
if context.spread.shape[1] > context.z_window:
# Keep only the z-score lookback period
spreads = context.spread[i, -context.z_window:]
zscore = (spreads[-1] - spreads.mean()) / spreads.std()
if context.inShort[i] and zscore < 0.0:
context.target_weights[stock_y] = 0
context.target_weights[stock_x] = 0
context.inShort[i] = False
context.inLong[i] = False
record(X_pct=0, Y_pct=0)
allocate(context, data)
return
if context.inLong[i] and zscore > 0.0:
context.target_weights[stock_y] = 0
context.target_weights[stock_x] = 0
context.inShort[i] = False
context.inLong[i] = False
record(X_pct=0, Y_pct=0)
allocate(context, data)
return
if zscore < -1.0 and (not context.inLong[i]):
# Only trade if NOT already in a trade
y_target_shares = 1
X_target_shares = -hedge
context.inLong[i] = True
context.inShort[i] = False
(y_target_pct, x_target_pct) = computeHoldingsPct(y_target_shares, X_target_shares, Y[-1], X[-1])
context.target_weights[stock_y] = y_target_pct * (1.0 / context.num_pairs)
context.target_weights[stock_x] = x_target_pct * (1.0 / context.num_pairs)
record(Y_pct=y_target_pct, X_pct=x_target_pct)
allocate(context, data)
return
if zscore > 1.0 and (not context.inShort[i]):
# Only trade if NOT already in a trade
y_target_shares = -1
X_target_shares = hedge
context.inShort[i] = True
context.inLong[i] = False
(y_target_pct, x_target_pct) = computeHoldingsPct(y_target_shares, X_target_shares, Y[-1], X[-1])
context.target_weights[stock_y] = y_target_pct * (1.0 / context.num_pairs)
context.target_weights[stock_x] = x_target_pct * (1.0 / context.num_pairs)
record(Y_pct=y_target_pct, X_pct=x_target_pct)
allocate(context, data)
return
context.spread = np.hstack([context.spread, new_spreads])
def hedge_ratio(Y, X, add_const=True):
if add_const:
X = sm.add_constant(X)
model = sm.OLS(Y, X).fit()
return model.params[1]
model = sm.OLS(Y, X).fit()
return model.params.values
def computeHoldingsPct(yShares, xShares, yPrice, xPrice):
yDol = yShares * yPrice
xDol = xShares * xPrice
notionalDol = abs(yDol) + abs(xDol)
y_target_pct = yDol / notionalDol
x_target_pct = xDol / notionalDol
return (y_target_pct, x_target_pct)
def get_current_portfolio_weights(context, data):
positions = context.portfolio.positions
positions_index = pd.Index(positions)
share_counts = pd.Series(
index=positions_index,
data=[positions[asset].amount for asset in positions]
)
current_prices = data.current(positions_index, 'price')
current_weights = share_counts * current_prices / context.portfolio.portfolio_value
return current_weights.reindex(positions_index.union(context.stocks), fill_value=0.0)
def allocate(context, data):
# Set objective to match target weights as closely as possible, given constraints
objective = opt.TargetWeights(context.target_weights)
# Define constraints
constraints = []
constraints.append(opt.MaxGrossExposure(1.0))
algo.order_optimal_portfolio(
objective=objective,
constraints=constraints,
) 