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T), dtype=np.float64) for i in range(0,len(assets)): if (not np.all(np.isnan(log_close[:,i]))): y = np.copy(log_close[:,i]) # interpolate Na N, not forward-looking since we are regressing anyway try: mask = np.isnan(y) y[mask] = np.interp(np.flatnonzero(mask), np.flatnonzero(~mask), y[~mask]) slope, _, r, _, _ = stats.linregress(x, y) scores[i] = slope * 256.0 * r * r except: scores[i] = -1000.0 log.error("Regression error!") else: scores[i] = -1000.0 out[:] = scores def descending_rank(a): return a.argsort()[::-1].argsort() # This is the momentum factor, except only calculated for those stocks which will end up # keeping within our set_screen.The ATR of is calculated using closing prices, which were around -60 in the 20 days leading up to the calculation date of 2007-12-05.This means the algorithm correctly calculates it must purchase 50 shares, worth around 00.The new price is , and all past price data is retroactively updated 2:1 (volume data is correspondingly updated 1:2).In effect, that means you can ignore stock splits unless you are storing prices in a live trading algorithm.Position sizing is critical to the success of this (and most algorithms), and ATR position sizing is pretty common. Since ATR isn't used in te actual pre-universe screening, only for actual position sizing, it would be simplest to just calculate ATR on data from 'history()' and use that to determine position sizes.
Is there a version of order_target that is compatible with pipeline?
I'm having trouble debugging the ATR Custom Factor, as I'm not sure how to slice into the arrays using the index columns to get the window of prices for BTU.
class ATR(Custom Factor): [...] btu_sid = symbol(' BTU')def compute(self, today, assets, out, close, high, low): btu_idx = assets.get_loc(btu_sid) close[btu_idx] # use me like this to get BTU prices [...] Our [quantopian's] data uses adjusted close prices.
# Implementation of the Stocks On The Move system by Andreas Clenow # import numpy as np import pandas as pd import scipy.stats as stats import time from quantopian.pipeline import Pipeline from quantopian.pipeline import Custom Factor from quantopian.import morningstar from quantopian.pipeline.factors import EWMA, Latest, Simple Moving Average from quantopian.builtin import USEquity Pricing from quantopian.algorithm import attach_pipeline, pipeline_output Universe Size = 500 Daily Range Per Stock = 0.001 # targeting 10bp of account value Rebalance Threshold = 0.005 # don't rebalance if the difference is less than 50bp of account value # This is the momentum factor, which is the 'slope' of an exponential regression # (ie a linear regression of logarithms), multiplied the the R-Squared of that regression class Momentum(Custom Factor): inputs = [USEquity Pricing.close] window_length = 90 def compute(self, today, assets, out, close): x = pd.
Series(range(0,self.window_length)) log_close = np.log(close) scores = np.empty(len(close.